Bibliographic record
Abstract
La littérature sur l’enseignement de la recherche qualitative pose sans ambages que la pratique est encore le meilleur des maîtres. Ce constat, qui semble aussi s’étendre à l’enseignement de l’éthique de la recherche, n’est pas sans soulever de nombreux dilemmes. Par exemple, est-il acceptable pour des étudiants de premier cycle universitaire de mener un court projet de recherche sans l’approbation institutionnelle d’un comité d’éthique ? Peut-on se « servir » d’individus, y compris d’individus potentiellement vulnérables, pour se faire la main à l’observation ou à l’entretien ? Faut-il prémunir les étudiants des chocs qui les attendent sur le terrain ? Enfin, certaines postures épistémologiques sont-elles plus acceptables éthiquement que d’autres ? Cet article aborde ces différents enjeux à partir de cas qui se sont présentés au fil de mon enseignement tout en cherchant à les inscrire dans la perspective d’une « intentionnalité éthique dans la recherche plutôt que de la recherche » (Sabourin, 2009 : 66).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Research integrity Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.206 | 0.226 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.009 | 0.033 |
| Scholarly communication | 0.020 | 0.016 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".